The Influence of Target and Perceiver Race in the Categorisation of Male Sexual Orientation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Although sexual orientation can be judged from faces, in previous work Caucasian or racially unspecified targets and perceivers have been primarily studied. Here, target and perceiver race were considered in the accurate categorisation of male sexual orientation. Asian, Black, and Caucasian participants categorised the sexual orientations of Asian, Black, and Caucasian men. Accuracy was significantly above chance and consistent across all combinations of perceivers and targets. Response bias scores showed that targets were significantly more likely to be categorised as straight, rather than gay, regardless of target or perceiver race. Moreover, judgments of individual targets were significantly correlated for perceivers from all three groups, suggesting cross-race consistency in target legibility. The perception of sexual orientation from faces therefore appears to be robust against variations in target and perceiver race.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it